Neural Network Pruning

Neural network pruning aims to reduce the computational cost and memory footprint of deep learning models by removing less important parameters, while preserving or even improving accuracy. Current research focuses on developing efficient pruning algorithms, including those based on reinforcement learning, Bayesian inference, and iterative methods, often applied to convolutional neural networks (CNNs) and transformer architectures. These advancements are significant because they enable the deployment of larger, more powerful models on resource-constrained devices and improve the efficiency of training and inference, impacting various fields from computer vision to natural language processing. Furthermore, research is actively exploring the interplay between pruning and model robustness, interpretability, and privacy.